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Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.
Vaccinations are a crucial intervention in combating infectious diseases. The three neurotropic Alphaviruses, Eastern (EEEV), Venezuelan (VEEV), and Western (WEEV) equine encephalitis viruses, are pathogens of interest for animal health, public health, and biological defense. In both equines and humans, these viruses can cause febrile illness that may progress to encephalitis. Currently, there are no licensed treatments or vaccines available for these viruses in humans. Experimental vaccines have shown variable efficacy and may cause severe adverse effects. Here, we outline recent strategies used to generate vaccines against EEEV, VEEV, and WEEV with an emphasis on virus-vectored and plasmid DNA delivery. Despite candidate vaccines protecting against one of the three viruses, few studies have demonstrated an effective trivalent vaccine. We evaluated the potential of published vaccines to generate cross-reactive protective responses by comparing DNA vaccine sequences to a set of EEEV, VEEV, and WEEV genomes and determining the vaccine coverages of potential epitopes. Finally, we discuss future directions in the development of vaccines to combat EEEV, VEEV, and WEEV.
Health providers/researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. The field of geospatial health remains in its infancy, and this program will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential. NASA will discuss some of their Public Health Projects and also providing the audience with information on opportunities for future collaborations with NASA for future research.
Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has been characterized by unprecedented rates of spatio-temporal spread. Here, we summarize the main events in the pandemic's timeline and evaluate what has been learnt by the public health community. We also discuss the implications for future public health policy and, specifically, the practice of epidemic control. We critically analyze this ongoing pandemic's timeline and contrast it with the 2002–2003 SARS outbreak. We identify specific areas (e.g., pathogen identification and initial reporting) wherein the international community learnt valuable lessons from the SARS outbreak. However, we also identify the key areas where international public health policy failed leading to the exponential spread of the pandemic. We outline a clear agenda for improved pandemic control in the future.
In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.
With the help of satellites, the Earth's environment can be monitored from a distance. Earth observing satellites and sensors collect data and survey patterns that supply important information about the environment relating to its affect on human health. Combined with ground data, such patterns and remote sensing data can be essential to public health applications. Remote sensing technology is providing information that can help predict factors that affect human health, such as disease, drought, famine, and floods. A number of public health concerns that affect Earth's human population are part of the current National Aeronautics and Space Administration (NASA) Earth Science Applications Plan to provide remotely gathered data to public health decision-makers to aid in forming and implementing policy to protect human health and preserve well-being. These areas of concern are: air quality; water quality; weather and climate change; infectious, zoonotic, and vector-borne disease; sunshine; food resource security; and health risks associated with the built environment. Collaborations within the Earth Science Applications Plan join local, state, national, or global organizations and agencies as partners. These partnerships engage in projects that strive to understand the connection between the environment and health. The important outcome is to put this understanding to use through enhancement of decision support tools that aid policy and management decisions on environmental health risks. Future plans will further employ developed models in formats that are compatible and accessible to all public health organizations.
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.
The first International Telemedicine/Disaster Medicine Conference was held in Dec. 1991. The overall purpose was to convene an international, multidisciplinary gathering of experts to discuss the emerging field of telemedicine and assess its future directions; principally the application of space technology to disaster response and management, but also to clinical medicine, remote health care, public health, and other needs. This collection is intended to acquaint the reader with recent landmark efforts in telemedicine as applied to disaster management and remote health care, the technical requirements of telemedicine systems, the application of telemedicine and telehealth in the U.S. space program, and the social and humanitarian dimensions of this area of medicine.
Objectives. To provide an overview of four environmental public health surveillance projects developed by CDC and its partners for the Health and Environment Linked for Information Exchange, Atlanta (HELIX-Atlanta) and to illustrate common issues and challenges encountered in developing an environmental public health tracking system. Methods. HELIX-Atlanta, initiated in October 2003 to develop data linkage and analysis methods that can be used by the National Environmental Public Health Tracking Network (Tracking Network), conducted four projects. We highlight the projects' work, assess attainment of the HELIX-Atlanta goals and discuss three surveillance attributes. Results. Among the major challenges was the complexity of analytic issues which required multidiscipline teams with technical expertise. This expertise and the data resided across multiple organizations. Conclusions:Establishing formal procedures for sharing data, defining data analysis standards and automating analyses, and committing staff with appropriate expertise is needed to support wide implementation of environmental public health tracking.
The Public Health application area focuses on Earth science applications to public health and safety, particularly regarding infectious disease, emergency preparedness and response, and environmental health issues. The application explores issues of toxic and pathogenic exposure, as well as natural and man-made hazards and their effects, for risk characterization/mitigation and improvements to health and safety. The program elements of the NASA Applied Sciences Program are: Agricultural Efficiency, Air Quality, Climate, Disaster Management, Ecological Forecasting, Water Resources, Weather, and Public Health.
This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities
These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.